Text Classification
Transformers
TensorFlow
Indonesian
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use iqbalpurba26/IndoBERT-topic-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iqbalpurba26/IndoBERT-topic-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="iqbalpurba26/IndoBERT-topic-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("iqbalpurba26/IndoBERT-topic-classification") model = AutoModelForSequenceClassification.from_pretrained("iqbalpurba26/IndoBERT-topic-classification") - Notebooks
- Google Colab
- Kaggle
IndoBERT_intent_classification
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.46.2
- TensorFlow 2.17.1
- Tokenizers 0.20.3
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Model tree for iqbalpurba26/IndoBERT-topic-classification
Base model
indobenchmark/indobert-base-p1